DNA methylation & adversity: pathways from exposures to health inequities

项目摘要 解决PAR-16-355的目标是“扩展理解表观遗传机制的方法” 我们的研究将通过哪些社会因素导致影响健康差距的生物学变化来进行 基于群体的发现和复制分析，以研究DNA甲基化(DNaM)如何变化 暴露于种族歧视、经济困难和空气污染，以及对差异的影响 心脏代谢性疾病风险和加速衰老(定义为表观遗传年龄&GT；时序年龄)。 在发现阶段，我们将从我的身体，我的故事中存储的血点中新获得dNaM (MBMS)研究(R01 AG027122)，其参与者包括1005名在美国出生的非 西班牙裔黑人(n=504)和非西班牙裔白人(n=501)成年人，年龄从35岁到，从4个 马萨诸塞州波士顿社区卫生中心(2008-2010)。这项研究有关于以下方面的丰富数据：(A)种族歧视 和社会经济地位，每一个都是在多个层面和整个生命过程中衡量的，加上住宅空气 (B)心脏代谢结果；(C)相关协变量。对于复制，我们将 使用新获得的类似dNaM、暴露和健康数据，这些数据来自第5波(2010-2012)的子集 动脉粥样硬化(MESA)多种族研究的参与者(N=1264，年龄55-94岁；582名非西班牙裔 白人，270名非西班牙裔黑人；404名西班牙裔，来自马里兰州巴尔的摩、北卡罗来纳州福赛斯县、纽约市和圣彼得堡。 明尼苏达州保罗)。为了加强基于横断面数据的因果推断，我们将使用MR-Base，一个 最近构建了高度强大的应用程序，利用公开可用的全基因组关联 研究(GWAS)汇总数据和孟德尔随机化(MR)以评估相关性的因果方向 暴露、dNaM和健康结果之间的关系。因此，我们的具体目标是： 目标1：进行新的分析，以确定dNaM的变异与以下方面有关：(1)目标1.1：暴露于 种族歧视、经济困难和空气污染；和(2)目标1.2：测量心脏代谢 结果(血压、空腹胰岛素、2型糖尿病、弗雷明翰心血管疾病10年 风险评分、代谢综合征)；加上(3)目标1.3：针对dNaM-在AIM中观察到的健康结果关联 1.2，使用MR-Base来加强对关联方向(例如甲基化)的因果推断 原因与疾病)；发现分析：MBMS；复制分析：MESA。 目标2：评估目标1研究暴露与健康结果之间的关系 加速衰老(表观遗传年龄&gt；时间年龄)，由三个新发现的dNaM确定 “时钟”(Horvath、Hannum和dNaM PhenoAge)；发现：MBMS；复制：MESA。 目标3：分析甲基化位点和“时钟”是否与研究暴露和健康有关 结果调解了这些暴露-结果关联，从而助长了健康不平等。 影响：结果将促进对造成健康差距的dNaM机制的了解。

Project Summary Addressing the objective of PAR-16-355 to “expand approaches for understanding epigenetic mechanisms by which social factors lead to biological changes that affect health disparities,” our study will conduct novel population-based discovery and replication analyses to investigate how DNA methylation (DNAm) varies with exposure to racial discrimination, economic hardship, and air pollution, and implications for disparities in cardiometabolic disease risk and accelerated aging (defined as epigenetic age > chronological age). For the discovery phase, we will newly obtain DNAm from the stored blood spots of our My Body, My Story (MBMS) study (R01 AG027122), whose participants comprise a random sample of 1005 US-born non- Hispanic black (n = 504) and non-Hispanic white (n = 501) adults, aged 35 to 64 years, recruited from four community health centers in Boston, MA (2008-2010). This study has rich data on: (a) racial discrimination and socioeconomic position, each measured at multiple levels and across the life-course, plus residential air pollution in the past year; (b) cardiometabolic outcomes; and (c) relevant covariates. For replication, we will use the newly available analogous DNAm, exposure, and health data from a subset of Wave 5 (2010-2012) participants in the Multi-Ethnic Study of Atherosclerosis (MESA) (N = 1264, age 55-94; 582 non-Hispanic white, 270 non-Hispanic black; 404 Hispanic, from Baltimore, MD, Forsyth County, NC, NYC, NY, and St. Paul, MN). To strengthen causal inference based on the cross-sectional data, we will employ MR-Base, a recently constructed highly powerful application that harnesses publicly-available genome-wide association study (GWAS) summary data and Mendelian Randomization (MR) to assess causal directions of associa- tions between exposures, DNAm, and health outcomes. Our Specific Aims thus are: Aim 1: Conduct novel analyses to identify variation in DNAm associated with: (1) Aim 1.1: exposure to racial discrimination, economic hardship, and air pollution; and (2) Aim 1.2: measured cardiometabolic outcomes (blood pressure, fasting insulin, Type 2 diabetes, Framingham Cardiovascular Disease 10-year risk score, metabolic syndrome); plus (3) Aim 1.3: For DNAm-health outcome associations observed in Aim 1.2, use MR-Base to strengthen causal inference about the direction of the associations (e.g., methylation causes vs. is due to disease); discovery analyses: MBMS; replication analyses: MESA. Aim 2: Assess the relationships between both the Aim 1 study exposures and health outcomes with accelerated aging (epigenetic age > chronological age), as identified by three newly identified DNAm “clocks” (Horvath, Hannum, and DNAm PhenoAge); discovery: MBMS; replication: MESA. Aim 3: Analyze if the methylation sites and “clocks” associated with both the study exposures and health outcomes mediate these exposure-outcomes associations, thereby contributing to health inequities. Impact: Results will advance knowledge about DNAm mechanisms contributing to health disparities.